Factors Relating to Successful Recruitment of Women in Information Technology Jobs

科学，技术，工程和数学（STEM）领域是经济增长和国家竞争力的关键驱动力。然而，妇女在许多STEM领域的代表性仍然不足，这对平等以及创造力和创新产生了巨大的负面影响，而创造力和创新往往受益于不同的观点。该奖项开发知识，增加妇女在STEM领域的代表性，重点是信息技术（IT）。研究围绕两个项目展开。项目1审查了招聘广告中所描述的信息技术工作的特点，这些特点增加了妇女申请的可能性。项目2审查要求雇主支付中期产假的立法是否改变了妇女申请工作的可能性和公司面试她们的可能性。调查结果为公司提供了吸引女性申请人的职位说明方面的信息，也为政策制定者提供了设计育儿假政策方面的信息。该奖项包括两个旨在促进妇女在STEM领域的代表性的项目。它使用来自电子招聘平台的大规模数据-包括约13，000家公司发布的约600万份申请到约20，000份IT招聘广告-可以通过招聘渠道跟踪女性的进步。项目1使用自然语言处理和机器学习，确定了增加妇女申请可能性的招聘广告的特点。该项目的调查结果扩大了关于妇女参与劳动力的文献，特别是关于吸引妇女的职务说明。由于以前的工作使用演绎的方法，它必然只考虑了一小部分潜在的相关特征。该奖项使用归纳方法来构建理论，利用“大”数据来揭示难以先验假设的关系。项目2采用一项事件研究来审查规定雇主支付中期产假的立法的影响。该项目的研究结果扩大了关于妇女劳动力参与的文献，特别是关于政策干预的文献。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Science, technology, engineering, and mathematics (STEM) fields are critical drivers of economic growth and national competitiveness. Yet women remain underrepresented in many STEM fields, which has dramatic, negative consequences both for equality and for creativity and innovation, which often benefit from diverse perspectives. This award develops knowledge that increases women’s representation in STEM fields, focusing on information technology (IT). The research is organized around two projects. Project 1 examines the characteristics of IT jobs, as described in job advertisements, that increase women’s likelihood of applying. Project 2 examines whether legislation requiring medium-term, employer-paid maternity leave changes women’s likelihood of applying to jobs and companies’ likelihood of interviewing them. The findings inform companies regarding the job descriptions that attract female applicants and policymakers regarding the design of parental-leave policies. This award includes two projects aimed at promoting women’s representation in STEM fields. It uses large scale data from an e-recruiting platform—including ~6 million applications to ~20,000 IT job advertisements posted by ~13,000 companies—that allows tracking of women’s progress through the hiring funnel. Project 1 identifies characteristics of job advertisements that increase the likelihood of women applying, using natural language processing and machine learning. Findings from this project expand the literature on women’s workforce participation, specifically regarding the job descriptions that appeal to women. Since prior work uses a deductive approach, it has necessarily considered only a small set of potentially relevant characteristics. This award instead uses an inductive approach to build theory, leveraging “big” data to uncover relationships that are hard to hypothesize a priori. Project 2 uses an event study to examine the effects of legislation mandating medium-term employer-paid maternity leave. Findings from this project expand the literature on women’s workforce participation, specifically regarding policy interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.